"After running the following Code…"
gbm = h2o.get_model(sorted_final_grid.sorted_metric_table()['model_ids'][0])
params = gbm.params
new_params = {"nfolds":5, "model_id":None}
for key in new_params.keys():
params[key]['actual'] = new_params[key]
gbm_best = H2OGradientBoostingEstimator()
for key in params.keys():
if key in dir(gbm_best) and getattr(gbm_best,key) != params[key]['actual']:
setattr(gbm_best,key,params[key]['actual'])
"I get the following error…H2OTypeError: 'training_frame' must be a valid H2OFrame!
It is a valid H2OFrame as I have not only imported using the import_file but also ran successfully all the GBM hyperparameter tuning code until I ran into this error.
I am using Python 3.6. I have been following this particular notebook https://github.com/h2oai/h2o-3/blob/master/h2o-docs/src/product/tutorials/gbm/gbmTuning.ipynb "